The invention relates to the technical field of determining the egomotion of mobile objects, such as e.g. vehicles, aircraft, robots and the like. In the case of the autonomous movement of such objects it is necessary to determine the position and orientation of the object in space, e.g. in a two-dimensional plane of movement. Measurement sensors disposed on the object, laser scanners for example, are used for the position determination. Said sensors detect the environment of the object and by this means generate a set of measurement points which result for example from the roundtrip time of laser beams of a laser scanner reflected by objects in the environment.
When laser scanners are used to determine position they are generally mounted in such a way that they allow a light beam to circulate parallel to the floor and measure the distance to the next reflecting point. In this way a sequence of two-dimensional measurement points in the coordinates of the autonomous object is obtained, said sequence also being referred to as a scan. In order to determine the egomotion of the object from said data it is known from the prior art to compare a scan performed at an earlier point in time with a current scan. In this case a search is conducted for a transformation which maps said two scans as accurately as possible onto one another.
In the prior art there are various standard methods for determining the egomotion of objects based on the comparison of sets of measurement points generated at different times. An overview of current methods can be found in publication [1].
The known methods can essentially be divided into two groups. The first group of methods extracts structural elements, in particular lines, from the measurement point sets and searches for a transformation which maps said structural elements as effectively as possible onto one another. The second group processes raw data in the form of the acquired measurement points. The first group operates precisely and reliably in structured environments, such as e.g. in interior spaces. The second group, in contrast, is suitable for universal use, though it is imprecise and more computationally intensive. Both method classes function poorly if the environment is only partially structured, for example if only a single wall can be extracted. In this case a method according to the first group cannot extract a displacement of the object parallel to the wall, whereas a method of the second group tends to rely on randomly, yet incorrectly assigned measurement point pairs of the measurement point sets that are compared with one another.